Defining the molecular details and consequences of the association of water-soluble proteins with membranes is fundamental to understanding protein-lipid interactions and membrane functioning. Phospholipase A 2 (PLA 2 ) enzymes, which catalyze the hydrolysis of phospholipid substrates that compose the membrane bilayers, provide the ideal system for studying protein-lipid interactions. Our study focuses on understanding the catalytic cycle of two different human PLA 2 s: the cytosolic Group IVA cPLA 2 and calcium-independent Group VIA iPLA 2 . Computer-aided techniques guided by deuterium exchange mass spectrometry data, were used to create structural complexes of each enzyme with a single phospholipid substrate molecule, whereas the substrate extraction process was studied using steered molecular dynamics simulations. Molecular dynamic simulations of the enzyme-substrate-membrane systems revealed important information about the mechanisms by which these enzymes associate with the membrane and then extract and bind their phospholipid substrate. Our data support the hypothesis that the membrane acts as an allosteric ligand that binds at the allosteric site of the enzyme's interfacial surface, shifting its conformation from a closed (inactive) state in water to an open (active) state at the membrane interface.GIVA cPLA 2 | GVIA iPLA 2 | PAPC | MD simulations | DXMS

We demonstrate that lipidomics coupled with molecular dynamics reveal unique phospholipase A2 specificity toward membrane phospholipid substrates. We discovered unexpected headgroup and acyl-chain specificity for three major human phospholipases A2. The differences between each enzyme’s specificity, coupled with molecular dynamics-based structural and binding studies, revealed unique binding sites and interfacial surface binding moieties for each enzyme that explain the observed specificity at a hitherto inaccessible structural level. Surprisingly, we discovered that a unique hydrophobic binding site for the cleaved fatty acid dominates each enzyme’s specificity rather than its catalytic residues and polar headgroup binding site. Molecular dynamics simulations revealed the optimal phospholipid binding mode leading to a detailed understanding of the preference of cytosolic phospholipase A2 for cleavage of proinflammatory arachidonic acid, calcium-independent phospholipase A2, which is involved in membrane remodeling for cleavage of linoleic acid and for antibacterial secreted phospholipase A2 favoring linoleic acid, saturated fatty acids, and phosphatidylglycerol.

. De novo drug design is a computational approach that generates novel molecular structures from atomic building blocks with no a priori relationships. Conventional methods include structure-based and ligand-based design, which depend on the properties of the active site of a biological target or its known active binders, respectively. Artificial intelligence, including machine learning, is an emerging field that has positively impacted the drug discovery process. Deep reinforcement learning is a subdivision of machine learning that combines artificial neural networks with reinforcement-learning architectures. This method has successfully been employed to develop novel de novo drug design approaches using a variety of artificial networks including recurrent neural networks, convolutional neural networks, generative adversarial networks, and autoencoders. This review article summarizes advances in de novo drug design, from conventional growth algorithms to advanced machine-learning methodologies and highlights hot topics for further development.

Phospholipase A 2 (PLA 2) enzymes are the upstream regulators of the eicosanoid pathway liberating free arachidonic acid from the sn−2 position of membrane phospholipids. Increased levels of intracellular arachidonic acid serve as a substrate for the eicosanoid biosynthetic pathway enzymes including cyclooxygenases, lipoxygenases and cytochrome P450s that lead to inflammation. The Group IVA cytosolic (cPLA 2), Group VIA calcium-independent (iPLA 2), and Group V secreted (sPLA 2) are three well-characterized human enzymes that have been implicated in eicosanoid formation. In this review, we will introduce and summarize the regulation of catalytic activity and cellular localization, structural characteristics, interfacial activation and kinetics, substrate specificity, inhibitor binding and interactions, and the downstream implications for eicosanoid biosynthesis of these three important PLA 2 enzymes.

Identification of Endocrine Disrupting Chemicals is one of the important goals of environmental chemical hazard screening. We report on the development of validated in silico predictors of chemicals likely to cause Estrogen Receptor (ER)-mediated endocrine disruption to facilitate their prioritization for future screening. A database of relative binding affinity of a large number of ERα and/or ERβ ligands was assembled (546 for ERα and 137 for ERβ). Both single-task learning (STL) and multi-task learning (MTL) continuous Quantitative Structure-Activity Relationships (QSAR) models were developed for predicting ligand binding affinity to ERα or ERβ. High predictive accuracy was achieved for ERα binding affinity (MTL R2=0.71, STL R2=0.73). For ERβ binding affinity, MTL models were significantly more predictive (R2=0.53, p<0.05) than STL models. In addition, docking studies were performed on a set of ER agonists/antagonists (67 agonists and 39 antagonists for ERα, 48 agonists and 32 antagonists for ERβ, supplemented by putative decoys/non-binders) using the following ER structures (in complexes with respective ligands) retrieved from the Protein Data Bank: ERα agonist (PDB ID: 1L2I), ERα antagonist (PDB ID: 3DT3), ERβ agonist (PDB ID: 2NV7), ERβ antagonist (PDB ID: 1L2J). We found that all four ER conformations discriminated their corresponding ligands from presumed non-binders. Finally, both QSAR models and ER structures were employed in parallel to virtually screen several large libraries of environmental chemicals to derive a ligand- and structure-based prioritized list of putative estrogenic compounds to be used for in vitro and in vivo experimental validation.